63 lines
2.0 KiB
Python
63 lines
2.0 KiB
Python
import numpy as np
|
|
import time
|
|
import threading
|
|
|
|
def determinant_sequential(matrix):
|
|
return np.linalg.det(matrix)
|
|
|
|
def determinant_parallel(matrix, num_threads):
|
|
n = len(matrix)
|
|
if n == 1:
|
|
return matrix[0][0]
|
|
if n == 2:
|
|
return matrix[0][0] * matrix[1][1] - matrix[0][1] * matrix[1][0]
|
|
|
|
threads = []
|
|
results = [0] * num_threads
|
|
chunk_size = n // num_threads
|
|
|
|
def worker(thread_id):
|
|
start_index = thread_id * chunk_size
|
|
end_index = min((thread_id + 1) * chunk_size, n)
|
|
det_sum = 0
|
|
for i in range(start_index, end_index):
|
|
submatrix = np.delete(matrix, i, 0)
|
|
submatrix = np.delete(submatrix, 0, 1)
|
|
det_sum += (-1)**i * matrix[i][0] * determinant_sequential(submatrix)
|
|
results[thread_id] = det_sum
|
|
|
|
for i in range(num_threads):
|
|
thread = threading.Thread(target=worker, args=(i,))
|
|
threads.append(thread)
|
|
thread.start()
|
|
|
|
for thread in threads:
|
|
thread.join()
|
|
|
|
return sum(results)
|
|
|
|
sizes = [100, 300, 500]
|
|
num_threads = [1, 2, 4]
|
|
|
|
results = {}
|
|
|
|
for size in sizes:
|
|
matrix = np.random.rand(size, size)
|
|
results[size] = {}
|
|
for n_threads in num_threads:
|
|
start_time = time.time()
|
|
if n_threads == 1:
|
|
det = determinant_sequential(matrix)
|
|
else:
|
|
det = determinant_parallel(matrix, n_threads)
|
|
end_time = time.time()
|
|
results[size][n_threads] = end_time - start_time
|
|
print(f"Размер матрицы: {size}x{size}, Потоков: {n_threads}, Время: {end_time - start_time:.4f} сек.")
|
|
|
|
|
|
print("\n## Результаты бенчмарка:")
|
|
print("| Размер матрицы | 1 поток (последовательно) | 2 потока | 4 потока |")
|
|
for size, timings in results.items():
|
|
print(f"| {size}x{size} | {timings [1] :.4f} сек. | {timings.get(2, 'N/A'):.4f} сек. | {timings.get(4, 'N/A'):.4f} сек. |")
|
|
|